Visualization Community

Explore, create and analyze complex genomic data visualizations.

Contributing Organizations

Whitehead Institute Dana-Farber Cancer Institute National Cancer Institute German Cancer Research Center
Search through our growing number of publication quality visualizations

Search through our growing number of publication quality visualizations.

Easily identify genomic data visualizations associated with select pediatric cancers and other diseases via filtering on publication, project, institution or diagnosis.

Explore Visualizations
Create, review,  and make your research public

Create, review, and make your research public.

Any visualization you or your team create is private by default. This ensures privacy - P.I. approval is required to make visualizations publicly viewable once completed or published.

Build with the best.

Our state of the art tools render robust visualizations for quick analysis of simple or complex datasets.

Allelic imbalance (aicheck)

Allelic imbalance (aicheck)

Displays the allelic imbalance of the heterozygous SNP markers in a tumor genome as compared to this patient’s germline genome, as a way of indicating loss-of-heterozygosity.

Arc (hicstraw)

Arc (hicstraw)

Used to display chromatin capture data by focusing on the frequency of links between distant genomic regions.

BAM

BAM

Visualizes the aligned reads and assists in understanding depth and quality of genomically mapped features on a nucleotide scale.

BigWig

BigWig

This format shows the depths of reads in RNA seq, ChIP seq, or genomic sequence as aligned to a reference genome.

Expression Rank

Expression Rank

Displays normalized expression in a genomic context such that regions that are distinctly highly expressed can be identified.

Hi-C (hicstraw)

Hi-C (hicstraw)

The standard Hi-C visualization for chromatin conformation data highlighting observed linkages between distant genomic regions.

JSON BED (bedj)

JSON BED (bedj)

This customizable bed format shows defined genomic regions identified by ChIP seq, mass spec or other means.

Lollipop plot

Lollipop plot

Used for displaying the frequency and type of mutation for a given gene on the protein structure in the context of functional domains.

MA+ Volcano plot

MA+ Volcano plot

The MA plot contrasts the magnitude of expression (M) on the x axis against the amplitude (A) of change between two states. The volcano plot compares the amplitude of change (x-axis ) to the statistical reliability of that change (-log transformed p value, y-axis).

Mutation Landscape (heatmap)

Mutation Landscape (heatmap)

Used for displaying the frequency of various mutations, CNVs and SVs in cancer relevant genes across a cohort of samples from one or many diagnoses.

Profile gene value (profilegenevalue)

Profile gene value (profilegenevalue)

Used to display gene. expression results from RNA seq across a cohort of samples.

RNA splice junction (junction)

RNA splice junction (junction)

This visualization displays the number of mapped junction reads precisely along with a gene model and will include alternative splicing and isoforms if available.

Splice Junction Cohort (mdsjuction)

Splice Junction Cohort (mdsjuction)

This visualization displays the number and color codes the type of mapped junction reads precisely along with a gene model for multiple samples including canonical and non-canonical splice forms.

Stranded BigWig

Stranded BigWig

RNA-seq coverage data as a pair of bigWig tracks, with forward strand on top and reverse strand on bottom.

tSNE plot (Scatterplot)

tSNE plot (Scatterplot)

tSNE plots are a display that converts high dimensional data to an interpretable proximity plot such that similarity may be assessed across RNA-seq profiles.

UMAP (single cell)

UMAP (single cell)

A data driven dimensional reduction method used to display single cell RNA seq data from a large number of cells.

VCF

VCF

This track localizes the variant data on reference genomes and can render frequency data for cohorts.

GenomePaint

GenomePaint

GenomePaint is track-based visualization app that enables the ready comparison of genomic localized data from multiple methods including RNA seq, ChIP seq, HiC, SNV, SV, CNV, and methylation data.

Data Table Facet

Data Table Facet

Used to track and compare varied genomic features from complementary data types such as ChIP seq to reference genome features.

Organize your team's visualizations

Organize your team's visualizations.

Easily identify genomic data visualizations associated with select pediatric cancers and other diseases via filtering on publication, project, institution or diagnosis.

View your teams

From private research to published interactive figures.

Increase the profile of your published visualizations and accessibility of your figures.

Public
Published
Collections
Once reviewed by a P.I. or Team lead, a visualization can be made publicly accessible.
Once your research has been published, add your PMID to your visualization to link with the associated publication.
Organize multiple visualizations into a collection associated with your research.
Unify teams and labs together in one location

Unify teams and labs together in one location.

Easily showcase your work and evolve the community of research.

View Communities
St. Jude Cloud

Genomic and proteomic data and analysis resources for the global research community.

Genomics Platform

Sequencing Data and Analysis

Access and analyze the world’s most comprehensive repositories of pediatric cancer-related genomics data and analysis tools.

Learn More
Genomics Platform

Variant Knowledgbase

Browse the pediatric cancer variant knowledge base, curated by researchers at St. Jude including pathogenicity classification and variant recurrence.

Learn More
Genomics Platform

Interactive Figures

Create and share figures using St. Jude's unique genomic visualization tools such as ProteinPaint and GenomePaint.

Learn More
Create, review, and make your research public

Need more help?

Access our help guides whether it be on how to cite us, who to contact for support, or to find our curated examples with sample data to start generating your own visualizations.

Visit the Docs